Explain Data Warehouse Architecture.

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 Data Warehouse Architecture

Data warehouses often adopt a three-tier architecture, as presented in Figure below:



1. Data Sources: A data warehouse system uses heterogeneous sources of data either from operational databases or from some external sources.

2. Bottom Tier: The bottom tier of the architecture is the data warehouse database server. It is the relational database system. Data is fed into the bottom tier by some back-end tools and utilities. The back end tools and utilities perform the following functions:

  • Data extraction: gathers data from multiple, heterogeneous and external sources.
  • Data cleaning: Detect errors in data and correct them when possible.
  • Data transformation: converts data from legacy or host format to warehouse format.
  • Load: which sorts, summarizes, checks integrity, and builds indices and partitions.
  • Refresh: This involves updating data sources to the warehouse.


3. Middle Tier: Middle tier is an OLAP server that can be implemented using either the relational OLAP (ROLAP) model or the multidimensional OLAP (MOLAP) model.

  • ROLAP is an extended relational database management system. The ROLAP maps the operations on multidimensional data to standard relational operations.
  • MOLAP directly implements multidimensional data and operations.


4. Top Tier: The top tier is a front-end client layer. The top tier layer holds the following tools:

  • Query and Reporting tools: Production reporting tool.
  • Analysis tools: Prepare charts based on analysis.
  • Data mining tools: Discover hidden knowledge, patterns.

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